Seven AI Prompts for Analysing Your Customer Complaints

Somewhere in your complaint management system is the answer to a problem that's been bothering you for months. The information is already there, but no one has the time to read thousands of case records and piece it together.

That's changing. With various types of AI now accessible for all businesses, it’s possible to interrogate large volumes of complaint data using plain English. Rather than building reports or manually reviewing case records, you can ask questions about your complaints operation and receive answers based on your live data. And there's no need to extract or move anything to make this possible. Platforms that bring your core business systems and AI capabilities together in one place—like Aptean AppCentral—let you query the data already held securely in your complaint management system, without ever exporting it elsewhere.

Here are seven AI prompts you can use to analyse your customer complaints data.

Prompt 1: Which complaint types have been escalated most often over the last six months, and what are the most common reasons for escalation?

Escalating complaints is a costly exercise. It pulls your senior handlers away from other work, extends resolution times and may turn routine cases into regulatory referrals.

Running this prompt analyses your escalated cases and groups them by shared characteristics. You may find they cluster around a particular product or service line, share the same policy wording or reveal weaknesses in a particular process.

AI's ability to read and interpret large volumes of complaint data reveals relationships you'd never spot working through cases one at a time. Once you identify the root cause, you can stop firefighting each escalation individually and focus your resources on resolving the underlying issue.

Prompt 2: Compare the root causes of complaints this quarter with last quarter. Which issues have increased the most, which have declined and are any new root causes emerging?

The causes of complaints are constantly changing. Something that barely troubled customers three months ago could now be driving a significant proportion of your caseload. But those changes are difficult to spot when you're focused on resolving individual cases.

The DCA complaints surge in motor finance is a good example: in a short space of time, the industry received more than 2.5 million complaints, and car loans suddenly became the most complained-about financial product in the country.

Entering this AI prompt compares root causes across different time periods, showing which issues are becoming more common, which are declining and where new complaint drivers are emerging.

Acting on those insights early gives you the opportunity to update guidance, review processes or escalate recurring issues to another part of the business before they have a wider operational impact.

Prompt 3: Which complaint types take the longest to resolve, and which cases are most likely to put our SLA or regulatory deadlines at risk?

Your average resolution time won't tell you which complaints consume the most time, or why. Some cases take longer because they're genuinely complex; others spend days waiting for an approval, a handover or additional information. Those issues require very different responses.

This prompt analyses resolution times by complaint type, root cause and stage of the complaint handling process. You can identify which cases consistently take the longest to resolve, where delays occur and which complaint types are most likely to impact your SLA and regulatory deadlines.

With that information, you can remove unnecessary bottlenecks, review how work is allocated and prioritise improvements that reduce delays before they affect customer outcomes or compliance performance.

Prompt 4: Analyse complaints withdrawn at the resolution stage. What do these cases have in common, and are withdrawals concentrated around particular complaint types, products, channels, teams or handlers?

A retraction isn't always a win. Some customers withdraw their complaint because they're satisfied, but others withdraw it because they've become frustrated with the process or no longer believe they'll achieve the outcome they want. Under Consumer Duty, the retraction doesn't absolve you; the poor outcome still stands.

Running this prompt identifies recently retracted complaints and analyses what those cases have in common. You can compare them by complaint type, channel, product, business area or handler to understand whether withdrawals are concentrated in a particular part of your operation.

Understanding why complaints are withdrawn helps you distinguish between positive resolutions and warning signs. You can investigate whether customers are disengaging because of avoidable delays, inconsistent handling or other operational issues, then make targeted improvements to create a frictionless customer complaint journey.

Prompt 5: Which issues appear most frequently in customer feedback, and how often do they later become formal complaints? Highlight the themes most likely to escalate.

Customers often grumble before they formally complain. A lukewarm survey score or an offhand remark to an agent may turn into a full formal complaint if nobody follows up on it.

One of AI's key advantages is its ability to read and interpret unstructured feedback. This prompt identifies the themes that come up repeatedly, then shows how many later lead to formal complaints.

By understanding which issues escalate and which don't, you stop treating feedback and complaints as separate workstreams and start using the first to predict—and prevent—the second.

Prompt 6: Which complaints required the most rework before reaching a final outcome, what caused the additional work and are there recurring themes across these cases?

Every time a case is sent back for more evidence or rewritten before sign-off, it consumes time that could be spent resolving other complaints. A high level of rework often points to a wider issue, whether that's inconsistent investigations, unclear ownership or gaps in procedures.

Running this prompt identifies complaints that required repeated work before they were closed, then explains what those cases have in common. You might find that a particular complaint type regularly requires additional investigation, or that certain cases are being passed between teams before reaching the right person.

These findings give you something specific to improve. And reducing unnecessary rework shortens resolution times, helping teams work more efficiently and creating a smoother experience for customers.

Prompt 7: Identify similar complaints that received different outcomes across teams or channels. Explain the differences in handling or decision-making and highlight any recurring inconsistencies.

If similar complaints lead to different outcomes, it's hard to know whether your policies are being applied consistently. You may have teams interpreting guidance differently, variations in how evidence is assessed or processes that produce variable results. Left unchecked, those inconsistencies create unnecessary risk and make it harder to demonstrate fair, consistent outcomes.

Running this prompt compares customer complaints with similar characteristics and identifies where outcomes differ between teams, channels or individual handlers. Rather than manually reviewing hundreds of cases, you can quickly pinpoint problems and investigate the underlying causes.

Understanding where and why outcomes differ helps you review guidance, strengthen quality assurance and introduce coaching where it's needed. It also provides evidence that you're monitoring complaint outcomes consistently and taking action where issues are identified.

Put These AI Prompts Into Practice With Aptean Respond

The seven prompts we've shared demonstrate what's possible when AI capabilities are delivered alongsideyour complaint management system. Rather than relying on predefined reports or manually reviewing case records, you can ask questions of your complaint data in plain English and receive answers based on your live caseload.

Of course, insights like these are only as good as the data behind them. That's where a purpose-built complaints platform makes the difference: Aptean Respond captures every complaint, contact, decision and outcome in a consistent, structured format, giving AI a complete and reliable dataset to work from. Our complaints management platform has been used by regulated financial service organisations for more than 30 years, and is now part of Aptean AppCentral, our connected AI-powered workspace.

AppCentral's GenAI Query capability is coming to Respond, and will allow you to query complaint data using natural language, making it easier to investigate complaint trends, compare outcomes, identify recurring issues and support continuous improvement. And because GenAI Query works with the data that already resides in your Respond database, there'll be no need to export case records or share sensitive complaint information with external tools.

If you'd like to explore where AI can support your team, download our free Five-Step Guide to Using AI in Your Complaints Operation, which explores AI’s role across intake, case handling, workload management, quality assurance and root cause analysis.

Or, if you'd like to learn more about Aptean Respond and what's coming with GenAI Query, get in touch to arrange a demonstration with one of our product specialists.